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Quick Review of Linear Algebra Department of Mathematics Quick Review of Matrix and Real Linear Algebra KC Border Subject to constant revision Last major revision: December 12, 2016 v. 2016.12.30::16.10 Contents 1 Scalar fields ....................................... 1 2 Vector spaces ...................................... 1 2.1 The vector space Rn ................................ 2 2.2 Other examples of vector spaces ......................... 3 3 Some elementary consequences ............................ 4 3.1 Linear combinations and subspaces ....................... 4 3.2 Linear independence ................................ 5 3.3 Bases and dimension ............................... 5 3.4 Linear transformations .............................. 6 3.5 The coordinate mapping ............................. 7 4 Inner product ..................................... 7 4.1 The Parallelogram Law .............................. 9 4.2 Orthogonality ................................... 10 4.3 Orthogonal projection and orthogonal complements .............. 11 4.4 Orthogonality and alternatives .......................... 13 4.5 The geometry of the Euclidean inner product .................. 14 5 The dual of an inner product space ......................... 14 6 ⋆ Linear functionals on infinite dimensional spaces .................. 17 7 Linear transformations ................................ 18 7.1 Inverse of a linear transformation ........................ 19 7.2 Adjoint of a transformation ............................ 20 7.3 Orthogonal transformations ............................ 22 7.4 Symmetric transformations ............................ 23 8 Eigenvalues and eigenvectors ............................. 24 9 Matrices ........................................ 27 9.1 Matrix operations ................................. 27 9.2 Systems of linear equations ............................ 28 9.3 Matrix representation of a linear transformation ................ 30 9.4 Gershgorin’s Theorem ............................... 32 9.5 Matrix representation of a composition ..................... 34 KC Border Quick Review of Matrix and Real Linear Algebra 0 9.6 Change of basis .................................. 34 9.7 The Principal Axis Theorem ........................... 36 9.8 Simultaneous diagonalization ........................... 36 9.9 Trace ........................................ 38 9.10 Matrices and orthogonal projection ....................... 40 10 Quadratic forms .................................... 41 10.1 Diagonalization of quadratic forms ........................ 42 11 Determinants ...................................... 43 11.1 Determinants as multilinear forms ........................ 44 11.2 Some simple consequences ............................ 47 11.3 Minors and cofactors ............................... 48 11.4 Characteristic polynomials ............................ 52 11.5 The determinant as an “oriented volume” .................... 53 11.6 Computing inverses and determinants by Gauss’ method ........... 54 Foreword These notes have accreted piecemeal from courses in econometrics and microeconomics I have taught over the last thirty-something years, so the notation, hyphenation, and terminology may vary from section to section. They were originally compiled to be a centralized personal reference for finite-dimensional real vector spaces, although I occasionally mention complex or infinite-dimensional spaces. If you too find them useful, so much the better. There is asketchy index, which I think is better than none. For a thorough course on linear algebra I now recommend Axler [7]. My favorite reference for infinite-dimensional vector spaces is the Hitchhiker’s Guide [2], but it needn’t be your favorite. v. 2016.12.30::16.10 KC Border Quick Review of Matrix and Real Linear Algebra 1 1 Scalar fields A field is a collection of mathematical entities that we shall call scalars. There are two binary operations defined on scalars, addition and multiplication. We denote the sum of α and β by α + β, and the product by α · β or more simply αβ These operations satisfy the following familiar properties: F.1 (Commutativity of Addition and Multiplication) α + β = β + α, αβ = βα. F.2 (Associativity of Addition and Multiplication) (α + β) + γ = α + (β + γ), (αβ)γ = α(βγ). F.3 (Distributive Law) α(β + γ) = (αβ) + (αγ). F.4 (Existence of Additive and Multiplicative Identities) There are two distinct scalars, denoted 0 and 1, such that for every scalar α, we have α + 0 = α and 1α = α. F.5 (Existence of Additive Inverse) For each scalar α there exists a scalar β such that α + β = 0. We often write −α for such a β. F.5 (Existence of Multiplicative Inverse) For each nonzero scalar α there exists a scalar β such that αβ = 1. We often write α−1 for such a β. There are many elementary theorems that point out many things that you probably take for granted. For instance, • −α and α−1 are unique. This justifies the notation by the way. • −α = (−1) · α. (Here −1 is the scalar β that satisfies 1 + β = 0.) • α + γ = β + γ ⇐⇒ α = β. • 0 · α = 0. • αβ = 0 =⇒ [α = 0 or β = 0]. There are many more. See, for instance, Apostol [5, p. 18]. The most important scalar fields are the real numbers R, the rational numbers Q, and the field of complex numbers C. Computer scientists, e.g., Klein [16], are fond of the {0, 1} field sometimes known as GF(2) or the Boolean field. These notes are mostly concerned with the field of real numbers. This is not because the other fields are unimportant—it’s because I myself have limited use for the others, whichis probably a personal shortcoming. 2 Vector spaces Let K be a field of scalars—usually either the real numbers R or the complex numbers C, or occasionally the rationals Q. v. 2016.12.30::16.10 KC Border Quick Review of Matrix and Real Linear Algebra 2 1 Definition A vector space over K is a nonempty set V of vectors equipped with two operations, vector addition (x, y) 7→ x + y, and scalar multiplication (α, x) 7→ αx, where x, y ∈ V and α ∈ K. The operations satisfy: V.1 (Commutativity of Vector Addition) x + y = y + x V.2 (Associativity of Vector Addition) (x + y) + z = x + (y + z) V.3 (Existence of Zero Vector) There is a vector 0V , often denoted simply 0, satisfying x + 0 = x for every vector x. V.4 (Additive Inverse) x + (−1)x = 0 V.5 (Associativity of Scalar Multiplication) α(βx) = (αβ)x V.6 (Scalar Multiplication by 1) 1x = x V.7 (Distributive Law) α(x + y) = (αx) + (αy) V.8 (Distributive Law) (α + β)x = (αx) + (βx) The term real vector space refers to a vector space over the field of real numbers, and a complex vector space is a vector space over the field of complex numbers. The term linear space is a synonym for vector space. I shall try to adhere to the following notational conventions that are used by Gale [13]. Vectors are typically denoted by lower case Latin letters, and scalars are typically denoted by lower case Greek letters. When we have occasion to refer to the components of a vector in Rm, I will try to use the Greek letter corresponding to the Latin letter of the vector. For instance, x = (ξ1, . , ξm), y = (η1, . , ηm), z = (ζ1, . , ζm). Upper case Latin letters tend to denote matrices, and their entries will be denoted by the corresponding lower case Greek letters.a That said, these notes come from many different courses, and I may not have standardized everything. I’m sorry. aThe correspondence between Greek and Latin letters is somewhat arbitrary. For instance, one could make the case that Latin y corresponds to Greek υ, and not to Greek η. I should write out a guide. 2.1 The vector space Rn By far the most important example of a vector space, and indeed the mother of all vector n spaces, is the space R of ordered lists of n real numbers. Given ordered lists x = (ξ1, . , ξn) and y = (η1, . , ηn) the vector sum x + y is the ordered list (ξ1 + η1, . , ξn + ηn). The scalar product is given by the ordered list αx = (αξ1, . , αξn). The zero of this vector space is the ordered list 0 = (0,..., 0). It is a trivial matter to verify that the axioms above are satisfied by these operations. As a special case, the set of real numbers R is a real vector space. In Rn we identify several special vectors, the unit coordinate vectors. These are the th ordered lists ei that consist of zeroes except for a single 1 in the i position. We use the notation ei to denote this vector regardless of the length n of the list. v. 2016.12.30::16.10 KC Border Quick Review of Matrix and Real Linear Algebra 3 2.2 Other examples of vector spaces The following are also vector spaces. The definition of the vector operations is usually obvious. • {0} is a vector space, called the trivial vector space. A nontrivial vector space contains at least one nonzero vector. • The field K is a vector space over itself. • The set L1(I) of integrable real-valued functions on an interval I of the real line, ∫ {f : I → R : f(t) dt < ∞} I is a real vector space under the pointwise operations: (f + g)(t) = f(t) + g(t) and (αf)(t) = αf(t) for all t ∈ I.1 • The set L2(P ) of square-integrable random variables, EX2 < ∞ on the probability space (S, E,P ) is a real vector space. • The set of solutions to a homogeneous linear differential equation, e.g., f ′′ +αf ′ +βf = 0, is a real vector space. • The sequence spaces ℓp, { } ∑∞ ∈ N | p| ∞ ∞ ℓp = x = (ξ1, ξ2,... ) R : ξn < , 0 < p < { n=1 } ℓ∞ = x = (ξ1, ξ2,... ) : sup |ξn| < ∞ n are all real vector spaces. • The set M(m, n) of m × n real matrices is a real vector space. • The set of linear transformations from one vector space into another is a linear space. See Proposition 12 below. • We can even consider the set R of real numbers as an (infinite-dimensional, see below) vector space over the field Q of rational numbers. This leads to some very interesting (counter-)examples. 1See how quickly I forgot the convention that scalars are denoted by Greek letters. I used t here instead of τ. v. 2016.12.30::16.10 KC Border Quick Review of Matrix and Real Linear Algebra 4 3 Some elementary consequences Here are some simple consequences of the axioms that we shall use repeatedly without further comment.
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